Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Generalizable Indoor Human Activity Recognition Method Based on Micro-Doppler Corner Point Cloud and Dynamic Graph Learning
Through-the-wall radar (TWR) human activity recognition can be achieved by fusing micro-Doppler signature extraction and intelligent decision-making algorithms. However, limited by the insufficient priori of tester in pr…
Activity RecognitionDecision MakingGraph LearningGraph Neural Network+1Generalization from Starvation: Hints of Universality in LLM Knowledge Graph Learning
Motivated by interpretability and reliability, we investigate how neural networks represent knowledge during graph learning, We find hints of universality, where equivalent representations are learned across a range of m…
Graph LearningIn-Context LearningFederated Graph Learning for Cross-Domain Recommendation
Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer across source and target domains. However, many recent CDR models overlook crucial issues such as …
Graph AttentionGraph LearningTransfer LearningCluster-wise Graph Transformer with Dual-granularity Kernelized Attention
In the realm of graph learning, there is a category of methods that conceptualize graphs as hierarchical structures, utilizing node clustering to capture broader structural information. While generally effective, these m…
Graph LearningNode ClusteringFedGraph: A Research Library and Benchmark for Federated Graph Learning
Federated graph learning is an emerging field with significant practical challenges. While algorithms have been proposed to improve the accuracy of training graph neural networks, such as node classification on federated…
BenchmarkingFederated LearningGraph LearningNode ClassificationNetwork Topology Inference from Smooth Signals Under Partial Observability
Inferring network topology from smooth signals is a significant problem in data science and engineering. A common challenge in real-world scenarios is the availability of only partially observed nodes. While some studies…
Graph LearningDefense-as-a-Service: Black-box Shielding against Backdoored Graph Models
With the trend of large graph learning models, business owners tend to employ a model provided by a third party to deliver business services to users. However, these models might be backdoored, and malicious users can su…
Backdoor AttackClusteringGraph ClassificationGraph LearningMulti-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders
The insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is important to develop a learning framework that can capture more information in limited data and insuffic…
Graph LearningEnhancing Graph Self-Supervised Learning with Graph Interplay
Graph self-supervised learning (GSSL) has emerged as a compelling framework for extracting informative representations from graph-structured data without extensive reliance on labeled inputs. In this study, we introduce …
Graph LearningSelf-Supervised LearningSiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering
With the increasing prevalence of graph-structured data, multi-view graph clustering has been widely used in various downstream applications. Existing approaches primarily rely on a unified message passing mechanism, whi…
ClusteringGraph ClusteringGraph LearningHow to Make LLMs Strong Node Classifiers?
Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tasks. Following this trend, we propose a n…
Graph LearningNode ClassificationSemantic RetrievalPhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems
Solving partial differential equations (PDEs) serves as a cornerstone for modeling complex dynamical systems. Recent progresses have demonstrated grand benefits of data-driven neural-based models for predicting spatiotem…
Graph LearningHyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks
Identifying unusual brain activity is a crucial task in neuroscience research, as it aids in the early detection of brain disorders. It is common to represent brain networks as graphs, and researchers have developed vari…
Anomaly DetectionGraph LearningUnsupervised Anomaly DetectionGraph-Based Representation Learning of Neuronal Dynamics and Behavior
Understanding how neuronal networks reorganize in response to external stimuli and give rise to behavior is a central challenge in neuroscience and artificial intelligence. However, existing methods often fail to capture…
Computational EfficiencyGraph LearningRepresentation LearningGUNDAM: Aligning Large Language Models with Graph Understanding
Large Language Models (LLMs) have achieved impressive results in processing text data, which has sparked interest in applying these models beyond textual data, such as graphs. In the field of graph learning, there is a g…
AttributeGraph LearningKnowledge GraphsOne Node Per User: Node-Level Federated Learning for Graph Neural Networks
Graph Neural Networks (GNNs) training often necessitates gathering raw user data on a central server, which raises significant privacy concerns. Federated learning emerges as a solution, enabling collaborative model trai…
Federated LearningGraph LearningConvergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting
Labor market forecasting on talent demand and supply is essential for business management and economic development. With accurate and timely forecasts, employers can adapt their recruitment strategies to align with the e…
Federated LearningGraph LearningMeta-LearningPrivacy PreservingDeep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition
Human Activity Recognition (HAR) is a challenging, multi-label classification problem as activities may co-occur and sensor signals corresponding to the same activity may vary in different contexts (e.g., different devic…
Activity RecognitionGraph LearningHuman Activity RecognitionMulti-Label Classification+1On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
Heterophily, or the tendency of connected nodes in networks to have different class labels or dissimilar features, has been identified as challenging for many Graph Neural Network (GNN) models. While the challenges of ap…
Graph LearningGraph Neural NetworkLink PredictionNode Classification+1Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning
Unsupervised Multiplex Graph Learning (UMGL) aims to learn node representations on various edge types without manual labeling. However, existing research overlooks a key factor: the reliability of the graph structure. Re…
Contrastive LearningGraph LearningGraph structure learning